U.S. patent number 6,091,843 [Application Number 09/146,361] was granted by the patent office on 2000-07-18 for method of calibration and real-time analysis of particulates.
This patent grant is currently assigned to Greenvision Systems Ltd.. Invention is credited to Nadav Horesh, Danny S. Moshe.
United States Patent |
6,091,843 |
Horesh , et al. |
July 18, 2000 |
Method of calibration and real-time analysis of particulates
Abstract
A method of analyzing particles for chemical or biological
species. Spectral images of the particles are acquired. Targets are
identified in the images and are classified according to morphology
type and spectrum type. Each target is assigned a value of an
extensive property. A descriptor vector is formed, each element of
the descriptor vector being the sum of the extensive property
values for one target class. The descriptor vector is transformed
to a vector of mass concentrations of chemical species of interest,
or of number concentrations of biological species of interest,
using a relationship determined in a calibration procedure. In the
calibration procedure, spectral images of calibration samples of
known composition are acquired, and empirical morphology types and
spectrum types are inferred from the spectral images. Targets are
identified in the spectral images, classified according to
morphology type and spectrum type, and assigned values of an
extensive property. For each calibration sample, a calibration
descriptor vector and a calibration concentration vector is formed.
A collective relationship between the calibration descriptor
vectors and the calibration concentration vectors is found, either
by multivariate analysis or by training a neural network.
Inventors: |
Horesh; Nadav (Petah Tikva,
IL), Moshe; Danny S. (Kiryat Ono, IL) |
Assignee: |
Greenvision Systems Ltd. (Tel
Aviv, IL)
|
Family
ID: |
22517035 |
Appl.
No.: |
09/146,361 |
Filed: |
September 3, 1998 |
Current U.S.
Class: |
382/133;
250/461.1; 356/318; 382/128; 382/224; 435/4 |
Current CPC
Class: |
G01N
21/64 (20130101); G01N 15/0625 (20130101); G01N
15/1463 (20130101); G01N 2015/1497 (20130101); G01N
2015/1477 (20130101); G01N 2015/1488 (20130101) |
Current International
Class: |
G01N
21/64 (20060101); G06K 009/00 (); G06K 009/62 ();
G01N 021/64 () |
Field of
Search: |
;382/128,133,165,224,156,203,219 ;250/458.1,461.1 ;356/318
;435/4,6,7.23 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Bella; Matthew
Attorney, Agent or Firm: Friedman; Mark M.
Claims
What is claimed is:
1. A method of analyzing particles for a plurality of species,
comprising the steps of:
(a) providing:
(i) a plurality of morphology types;
(ii) a plurality of spectrum types;
(iii) a plurality of target classes, each of said target classes
corresponding to one of said morphology types and one of said
spectrum types, and
(iv) a relationship between a descriptor vector and a concentration
vector, said descriptor vector including a plurality of elements,
each element of said descriptor vector corresponding to a different
one of said target classes, said concentration vector including a
plurality of elements, each element of said concentration vector
corresponding to a different one of the species;
(b) acquiring a plurality of images of the particles, each of said
images being acquired at a different wavelength;
(c) inferring said descriptor vector from said plurality of images;
and
(d) using said relationship to infer said concentration vector from
said descriptor vector.
2. The method of claim 1, wherein said acquiring of said images is
effected using a scanning interferometer.
3. The method of claim 1, wherein said acquiring of said images is
effected using a liquid crystal tunable filter.
4. The method of claim 1, wherein said acquiring of said images is
effected using an acousto-optic tunable filter.
5. The method of claim 1, wherein said acquiring of said images is
effected using a spectrometer.
6. The method of claim 1, wherein said inferring of said descriptor
vector from said plurality of images includes the steps of:
(i) identifying a plurality of targets in said plurality of
images;
(ii) for each of said targets:
(A) associating said each target with one of said target classes,
and
(B) obtaining a value of an extensive property of said each
target;
(iii) for each of said target classes, summing said values of said
extensive property of said targets associated with said each target
class to obtain said element of said descriptor vector that
corresponds to said target class.
7. The method of claim 6, wherein each of said images includes a
plurality of pixels, each of said pixels having a location in said
each image, each of said pixels having an intensity, and wherein
said identifying of said plurality of targets includes the step
of:
(A) for each of said locations, summing said intensities of said
pixels that have said each location, thereby obtaining a summed
intensity.
8. The method of claim 7, wherein said identifying of said
plurality of targets further includes the steps of:
(B) for each of said locations for which said summed intensity
exceeds a threshold: matching said intensities of said pixels
having said each location to one of said spectrum types, thereby
obtaining a matched spectrum type; and
(C) for each said matched spectrum type, grouping adjoining said
locations that share said each matched spectrum type, thereby
obtaining one of said targets.
9. The method of claim 8, wherein said associating of said each
target with one of said target classes includes the steps of:
(I) determining at least one morphological parameter of said each
target; and
(II) matching said at least one morphological parameter with one of
said morphology types, thereby obtaining a matched morphology
type;
said each target then being associated with said target class
corresponding to said matched spectrum type and said matched
morphology type.
10. The method of claim 7, wherein said identifying of said
plurality of targets further includes the step of:
(B) grouping adjoining said locations for which said summed
intensity exceeds a threshold to obtain one of said targets.
11. The method of claim 10, wherein said associating of said each
target with one of said target classes includes the steps of:
(I) determining at least one morphological parameter of said each
target;
(II) matching said at least one morphological parameter with one of
said morphology types, thereby obtaining a matched morphology
types;
(III) for each location in said each target, matching said
intensities of said pixels having said each location to one of said
spectrum types, thereby obtaining a matched spectrum type; and
(IV) selecting, from among said matched spectrum types, a
representative spectrum type;
said each target then being associated with said target class
corresponding to said representative spectrum type and said matched
morphology type.
12. The method of claim 1, wherein said providing of said plurality
of morphology types is effected by steps including:
(A) providing at least one calibration sample of the particles;
(B) for each of said at least one calibration sample:
(I) acquiring a plurality of calibration images of the particles of
said each calibration sample, each of said calibration images being
acquired at a different wavelength,
(II) identifying a plurality of calibration targets in said
plurality of calibration images, and
(III) for each of said plurality of calibration targets:
determining at least one morphological parameter; and
(C) performing cluster analysis on said at least one morphological
parameter of said calibration targets of said at least one
calibration sample.
13. The method of claim 1, wherein said providing of said plurality
of spectral types is effected by steps including:
(A) providing at least one calibration sample of the particles;
(B) for each of said at least one calibration sample:
(I) acquiring a plurality of calibration images of the particles of
said each calibration sample, each of said calibration images being
acquired at a different wavelength, each of said calibration images
including a plurality of pixels, each of said pixels having a
location in said each calibration image, each of said pixels having
an intensity, and
(II) for each of said locations, summing said intensities of said
pixels that have said each location, thereby obtaining a summed
intensity; and
(C) performing cluster analysis on said intensities of said pixels
of said locations whose summed intensity exceeds a threshold.
14. The method of claim 1, wherein said providing of said
relationship between said descriptor vector and said concentration
vector is effected by steps including:
(A) providing at least one calibration sample of the particles;
(B) for each of said at least one calibration sample:
(I) acquiring a plurality of calibration images of the particles of
said
each calibration sample, each of said calibration images being
acquired at a different wavelength,
(II) inferring a calibration descriptor vector from said plurality
of images, said calibration descriptor vector including a plurality
of elements, each element of said descriptor vector corresponding
to a different one of said target classes,
(III) analyzing said each calibration sample to obtain a
concentration of each of the species, and
(IV) forming a calibration concentration vector, said calibration
concentration vector including a plurality of elements, each
element of said calibration concentration vector being a different
one of said concentrations; and
(C) inferring said relationship from said calibration descriptor
vectors and from said calibration concentration vectors.
15. The method of claim 14, wherein said inferring is effected by
multivariate analysis.
16. The method of claim 14, wherein said inferring is effected by
training a neural net.
17. The method of claim 1, wherein said providing of said
relationship between said descriptor vector and said concentration
vector is effected by steps including:
(A) providing at least one calibration sample of the particles for
which concentrations of the species are known;
(B) for each of said at least one calibration sample:
(I) acquiring a plurality of calibration images of the particles of
said each calibration sample, each of said calibration images being
acquired at a different wavelength,
(II) inferring a calibration descriptor vector from said plurality
of images, said calibration descriptor vector including a plurality
of elements, each element of said descriptor vector corresponding
to a different one of said target classes, and
(IV) forming a calibration concentration vector, said calibration
concentration vector including a plurality of elements, each
element of said calibration concentration vector being a different
one of said concentrations; and
(C) inferring said relationship from said calibration descriptor
vectors and from said calibration concentration vectors.
18. The method of claim 17, wherein said inferring is effected by
multivariate analysis.
19. The method of claim 17, wherein said inferring is effected by
training a neural net.
20. The method of claim 1, wherein said relationship is linear.
21. The method of claim 1, wherein said relationship is implemented
as a neural net.
22. The method of claim 1, wherein said acquiring of said plurality
of images is effected by steps including exciting the particles to
emit emitted light, each of said images being of said emitted
light.
23. The method of claim 22, wherein said exciting is effected by
directing excitation light at the particles.
Description
FIELD AND BACKGROUND OF THE INVENTION
The present invention relates to chemical analysis and, more
particularly, to on-line quantitative analysis of chemical species
in particulates. In particular, the present invention relates to
the on-line quantitation of polycyclic aromatic hydrocarbons (PAH)
and other fluorescent contaminants in aerosols.
PAH are among the many organic materials that are commonly
encountered as trace-level environmental contaminants in effluents
associated with incomplete combustion, pyrolysis and other thermal
degradation processes. The PAH family, defined as containing
hydrocarbon species with three or more fused aromatic rings,
includes many compounds suspected of being potent carcinogens.
Therefore, identification and determination of emission levels of
PAH is important in environmental assessment. Moreover, emission
monitoring of PAH compounds is of considerable industrial
importance as well, since several industrial processes can be
controlled by a fast feedback of PAH composition and
concentration.
Several procedures, such as gas chromatography/mass spectrometry
(GC-MS), have been developed and applied for obtaining compound
specific information for evaluation of PAH contamination. These
procedures cannot be applied directly to particulate PAH analysis,
because they all involve several sample preparation steps in which
the particles are destroyed. The GC-MS methods, in particular, are
complicated and expensive; they require state of the art high
vacuum equipment and extensive investment of expert analyst's time.
It is not cost effective to apply them routinely to samples that
may not, in fact contain any relevant levels of PAH. Moreover, the
GC-MS methods are not on-line methods for particulate analysis, and
cannot be used for obtaining fast feedback which is required for
both environmental protection and for industrial process
control.
PAH compounds are produced primarily as a result of incomplete
combustion of organic matter, and thus are believed to exist in
both the vapor phase and the solid phase, as an integral
constituent of particulate matter. Because the concentration of
such pollutants in most atmospheric samples is very low, and
because they are often associated with other contaminants, the
identification and quantification of PAH are usually complex, time
consuming and often inaccurate because of multistep isolation and
determination techniques. This problem is primarily associated with
analysis of PAH on aerosol particles, which is considered the most
complicated task for classical methods of PAH analysis.
Nevertheless, analysis of PAH on aerosols is of intense interest to
both industry and governmental environmental protection bodies. It
has been proven that most PAH mass is found onto aerosol particles,
rather than in the vapor phase. (This is because of the low vapor
pressure of many of these compounds at ambient temperature.) The
distribution of PAH as a function of aerodynamic diameter, for coke
oven emission, shows that most contamination is associated with
particles of diameter of 1-10 .mu.m. The absolute concentration of
PAH compounds an air is compound-dependent, and is usually in the
range of 0.02-0.2 .mu.g m.sup.-3. Absolute concentration in the
vicinity of industrial sites may be ten times higher, and
concentrations in the .mu.g m.sup.-3 and higher, of particles
having diameters between 10 and 100 .mu.m or more, have been
measured close to combustion chimneys.
Most of the currently employed analytical methods for PAH on
aerosols involve (a) collection of particulate PAH by drawing a
large volume of air through a filter, (b) extraction of the PAH
collected on a filter paper with an organic solvent, and (c)
chromatographic cleanup and separation followed by (d)
identification and quantitation using one or a combination of
spectroscopic and chromatographic methods, or mass spectrometry
analysis in a high vacuum chamber.
There are a number of analytical difficulties associated with these
traditional methods. The real-time analysis of PAH present in
ambient air (fumes, coke oven emission, smoke or other gaseous
media) cannot be achieved, mainly because of lack of selectivity,
sensitivity, and mobility of the analytical instrumentation.
Considering the above difficulties, and taking into account that
traditional methods do not provide on-line and in-situ results, it
follows that there is a widely recognized need for, and it would be
highly advantageous to have, a method for real-time, on-line
analysis of aerosol particles for PAH.
SUMMARY OF THE INVENTION
According to the present invention there is provided a method of
analyzing particles for a plurality of species, including the steps
of: (a) providing: (i) a plurality of morphology types; (ii) a
plurality of spectrum types; (iii) a plurality of target classes,
each of the target classes corresponding to one of the morphology
types and one of the spectrum types, and (iv) a relationship
between a descriptor vector and a concentration vector, the
descriptor vector including a plurality of elements, each element
of the descriptor vector corresponding to a different one of the
target classes, the concentration vector including a plurality of
elements, each element of the concentration vector corresponding to
a different one of the species; (b) acquiring a plurality of images
of the particles, each of the images being acquired at a different
wavelength; (c) inferring the descriptor vector from the plurality
of images; and (d) using the relationship to infer the
concentration vector from the descriptor vector.
The present invention is a method of quantification of species on
particles. The species may be either chemical species, such as PAH,
or biological species, particularly microorganisms such as bacteria
and
algae. In the latter case, the microorganism itself may be the
particle.
For definiteness, the description below focuses on the use of the
present invention for the quantitation of PAH in aerosol particles.
Therefore, in the description below, the images are of fluorescent
or phosphorescent light emitted by the particles, under excitation
by incident ultraviolet light, rather than of light reflected or
transmitted by the particles. Nevertheless, the scope of the
present invention includes the analysis of images of light
reflected or transmitted by the particles, in addition to the
analysis of light emitted by the particles in response to
excitation. Furthermore, the excitation may be by incident
electromagnetic radiation of any suitable wavelength, notably
visible and infrared light, or even by simply heating the
particles.
The particles to be analyzed are spread out on a two-dimensional
surface, so that each pixel in each two dimensional intensity image
represents a part of only one article. Generally, aerosol particles
collected on the surface of a filter, as in the prior rt method of
PAH analysis, are spread out appropriately. When the images are of
light emitted by the particles in response to incident light, there
are two general methods of acquiring the images. In the first
method, the surface to be imaged is irradiated homogeneously, and
the emitted light is transferred, via a suitable optical system, to
a spectroscopic imaging device. Examples of such devices are the
acousto-optic tunable filter and the scanning interferometer
described by Lewis et al. in U.S. Pat. No. 5,377,003, which is
incorporated by reference for all purposes as if fully set forth
herein; the scanning interferometer described by Cabib et al. in
U.S. Pat. No. 5,539,517 and produced by Applied Spectral Imaging,
Ltd. of Migdal Haemek, Israel, under the name "ASI SD2000", and the
liquid crystal tunable filter described in Fluorescence Imaging
Spectroscopy and Microscopy (Xue Feng Wang & Brian Herman,
editors, John Wiley & Sons, Inc., 1996). In the second method,
the surface to be imaged is scanned using a focused beam of light,
and the emitted light is analyzed by a conventional spectrometer.
Under both methods, the spectrally decomposed emitted light is
imaged by one of several methods. The straightforward method uses a
solid-state area image sensor array such as an array of charge
coupled detectors (CCD), with each detector of the array acquiring
one pixel of each image. Another method is to acquire each image
one row of pixels at a time using a scanning diode array. CCD
arrays recently have become available that are sufficiently dense
that several images corresponding to several different wavelengths
can be acquired simultaneously. For example, a 4096.times.4096 CCD
array can acquire 64 512.times.512 images simultaneously, at 64
different wavelengths. As an alternative to the spectrometers,
these large CCD arrays can be used with a large number (64 in the
example given) of narrow band optical filters to obtain
single-wavelength images. Under this alternative, the sample must
be moved, for example on a piezoelectric stage, from one filter to
another. In the analysis of aerosol particles for PAH, the optical
system includes a microscope, so that the final single-wavelength
images are sufficiently magnified to resolve the target particles
at the desired resolution of one or more pixels per particle.
The output of the image acquisition is, for each imaged portion of
the two-dimensional surface, a set of images, each image at a
different wavelength. These images are digitized and analyzed by
standard image processing methods to produce, for each imaged
portion of the two-dimensional surface, spectral images of targets.
Typically, each target corresponds to one particle, or, in the case
of images of PAH fluorescence, the portion of the surface of the
particle occupied by one PAH species. Each target is classified as
belonging to one of a standard set of morphology types and one of a
standard set of spectrum types. For each target, a value of an
extensive property, such as area or total intensity, is obtained.
These values are summed separately for each target class. The array
of summed extensive properties constitutes a collective descriptor
vector for all the targets. A relationship is provided that relates
the descriptor vector to a vector of concentrations of species of
interest. If the species of interest are chemical species, then the
concentrations are expressed as mass per unit area. If the species
of interest are biological species, then the concentrations are
expressed as number of organisms per unit area. This relationship
is used to infer the concentrations of the species of interest from
the descriptor vector.
The set of standard morphology types, the set of standard spectrum
types, and the relationship between descriptor vectors and
concentration vectors are obtained by a calibration procedure. A
set of calibration samples is provided. These calibration samples
may be collections of particles of known composition or collections
of particles of unknown composition but of the type that is to be
analyzed. For each calibration sample, one or more sets of images
at different wavelengths are acquired. Each image includes a
plurality of pixels. With each pixel is associated an intensity
value. The set of intensity values of pixels that have a common
location in the images of one set constitute a spectrum associated
with that location. Spectra whose summed intensity exceeds a
predetermined threshold are classified by cluster analysis to
obtain the standard spectrum types. See, for example, R. L. Kettig
and D. Landgrebe, "Classification of multispectral image data by
extraction and classification of homogeneous objects", IEEE
Transactions on Geoscience Electronics, Vol. GE14 p. 19 (1976).
Locations whose summed intensity exceeds the threshold are grouped
into calibration targets. For each calibration target, values of
morphological parameters such as area or aspect ratio is
calculated. The values of the morphological parameters are
classified by cluster analysis to obtain the standard morphology
types. Each calibration target also is classified as belonging to
one of the standard spectrum types. For each calibration target, a
value of an extensive parameter is obtained, and these values are
summed to provide a calibration descriptor vector for each, as
described above.
The calibration samples now are analyzed by a prior art method, if
necessary, to obtain, for each calibration sample, a calibration
concentration vector, each element of which is a value of the
concentration of a species of interest in the calibration sample.
The desired relationship between the calibration descriptor vectors
and the calibration concentration vectors now is determined by
standard computational methods, for example multivariate analysis
or by training a neural net. The output of multivariate analysis is
a linear transformation, expressed as a matrix, that relates
descriptor vectors to corresponding concentration vectors. The
descriptor vector is multiplied by this matrix to yield the
concentration vector. The output of the training of a neural net is
a trained neural net whose inputs are descriptor vectors and whose
outputs are corresponding concentration vectors.
With regard to analysis of chemical species, the present invention
is similar to the method of particulate analysis described in
co-pending U.S. patent application Ser. No. 08/790,696. The
significant differences between the present invention and U.S. Ser.
No. 08/790,696 are as follows:
1. In U.S. Ser. No. 08/790,696, the spectra in the database are
spectra of pure chemical species. In the present invention, the
standard spectra are determined empirically in the calibration
procedure. This is important in the case of PAH adsorbed on
aerosols, because the spectra of adsorbed chemical species in
general and of PAH in particular are known to be altered by the
surfaces on which they are adsorbed and by contaminants.
2. In U.S. Ser. No. 08/790,696, the shapes of the particles are
considered along with the spectra of the particles, but only in an
ad hoc manner. In the present invention, the relationship between
the descriptor vector and the concentration vector accounts
explicitly and simultaneously for both morphologies and empirically
determined spectra. This is particularly important in the case of
PAH adsorbed on aerosols, because the fluorescence spectra of PAH
crystals are known to depend on crystal morphology in general and
crystal size in particular.
BRIEF DESCRIPTION OF THE DRAWINGS
The invention is herein described, by way of example only, with
reference to the accompanying drawings, wherein:
FIG. 1 is a schematic diagram of a system for quantifying PAH in
aerosols;
FIG. 2 is a flow diagram of the detection and quantification of
PAH;
FIG. 3 is a flow diagram of the calibration of the quantification
method
FIGS. 4A and 4B are fluorescence spectra of algal species.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
The present invention is of a method of quantitative analysis of
chemical species in particulates which is based on an empirically
determined relationship among spectra, morphologies and
concentrations. Specifically, the present invention can be used for
real-time, on-line quantification of PAH in aerosols.
The principles and operation of particulate analysis according to
the present invention may be better understood with reference to
the drawings and the accompanying description.
Referring now to the drawings, FIG. 1 is a schematic diagram of an
automatic on-line real-time system for monitoring PAH in aerosols.
A roll of a non-fluorescing substrate 10 such as non-fluorescing
filter paper is mounted on a pair of rollers 12, which move
substrate 10 from left to right as seen in FIG. 1. A high volume
air pump 16 sucks in contaminated air via a pipe 14 and through
substrate 10, depositing aerosol particles 18 on substrate 10.
Optionally, a filtration system (not shown), such as a 10PM high
volume particle sampler, may be placed in pipe 14 to select
particles below a certain size, for example, 10.mu.. Rollers 12
move aerosol articles 18 to a position for viewing under a
spectroscopic imaging system 30 that includes a source of
ultraviolet light 20, an optical system 22, a spectroscopic imaging
device 24 and a CCD camera 26 having a suitable sensitivity and
dynamic range. Typical spectroscopic imaging systems are described,
for example, in the Lewis et al. patent cited above, and will not
be elaborated further herein.
Components 20, 22, 24 and 26 of spectroscopic imaging system 30 are
connected by suitable control/data links 32 to a control system 34.
Light source 20 illuminates particles 18 homogeneously via optical
system 22, as shown in FIGS. 6 and 8 of the Lewis et al. patent
cited above. In other embodiments of the present invention, light
source 22 directs ultraviolet light directly onto particles 18,
without the intervention of optical system 22. Rollers 12 also are
connected by a control/data link 32 to control system 34 so that
substrate 10 can be advanced under the control of control system
34. Rollers 12 are mounted on a stage 13 which has two degrees of
freedom of motion: laterally (into and out of the plane of FIG. 1)
and vertically. The vertical motion of stage 13 is used to effect
autofocusing. Stage 13 also is controlled by control system 34 via
a control/data link 32. The combined motions of rollers 12 and
stage 13 allow substrate 10 to be moved laterally in three
directions under optical system 22.
Control system 34 is based on a personal computer, and includes a
frame grabber, for acquiring images from camera 26, as well as
other hardware interface boards for controlling rollers 12, stage
13 and the other components 20, 22 and 24 of spectroscopic imaging
system 30. The software of control system 34 includes a database of
empirically determined morphology types and spectrum type and code
for implementing the image processing and quantification algorithms
described below.
Preferably, rollers 12 are used to move substrate 10 to the right,
as seen in FIG. 1, in a stepwise fashion, so that while control
system 34 is acquiring and analyzing images of one sample of
particles 18, pump 16 is collecting the next sample of particles
18. Rollers 12 and stage 13 also are used to move particles 18 a
much shorter distance laterally under optical system 22, to allow
control system 34 to acquire images from several fields of view in
a sample.
FIG. 2 is a flow diagram of the process of automatic detection and
quantification of PAH. By shifting the field of view laterally,
using rollers 12 and stage 13, images of all fields of view of the
sample are acquired (blocks 40 and 56). Within each field of view,
a set of images are acquired at the desired wavelengths (block 44)
and the single-wavelength images are summed to give a summed, or
gray level, image (block 46). Note that there is a one to one
correspondence between the pixels of the summed image and what is
referred to herein as the "common locations" of pixels of the
single-wavelength images.
Subsequent image processing analyzes the images in terms of
targets. Each target is a collection of pixels of single-wavelength
images whose summed-image pixels have: (a) intensities above a
preset threshold and (b) adjoining locations. The targets are
identified (block 50) and classified (block 52), and each target is
assigned a value of an extensive property (block 54).
The morphology types in the database are empirically determined
ranges of parameters used to characterize the morphologies of the
targets. For example, a set of targets could be described in terms
of areas and aspect ratios, with three area ranges:
<5 square microns (small)
5-50 square microns (medium)
>50 square microns (large)
and two aspect ratio ranges:
1 to 1.5 (round)
>1.5 (elongated).
The cross-product of these ranges gives six morphology types: small
round, small elongated, medium round, medium elongated, large round
and large elongated. Raw morphology types may be merged to fewer
types. For example, if the aspect ratios of small and large
particles are of no consequence, the six raw morphology types may
be merged to four: small ("sm"), medium round ("mr"), medium
elongated ("me"), and large ("lg").
The spectrum types in the database are empirically determined
normalized discrete functions of wavelength. Suppose that the
single-wavelength images are acquired at L discrete wavelengths
.lambda..sub.l. Then each standard spectrum S is a collection of
non-negative numbers s.sub.l, one per wavelength, normalized as
##EQU1##
The target classes are direct products of the morphology types and
the spectrum types. For example, if there are four morphology types
(sm, mr, me and lg) and three spectrum types (S.sub.A, S.sub.B and
S.sub.C) then there are twelve target classes.
There are two preferred methods for identifying targets (block 50)
and classifying targets (block 52). The first method takes into
account the spectra of the single-wavelength images, i.e., the
intensities of the pixels at common locations. Suppose that at one
location, the L pixels have intensities p.sub.l. Each location
whose summed intensity exceeds the threshold is classified by
spectrum type, by seeking the spectrum type that most closely
matches the location spectrum. One way of doing this matching is to
take the dot product of the location spectrum with each of the
spectrum types: ##EQU2## where .alpha. indexes the spectrum type.
The location is assigned the spectrum type whose dot product with
the location spectrum is largest. Another way of doing this
matching is to normalize the intensities p.sub.l to one, as in
equation (1), and then to compute the squared Euclidean distance
between the location spectrum and each of the spectrum types:
##EQU3## where p is the mean of the p.sub.l and s.sup..alpha. is
the mean of the s.sup..alpha..sub.l for each .alpha.. The location
is assigned the spectrum type whose Euclidean distance from the
location spectrum is smallest. Then, all adjoining locations of
identical spectrum type are grouped together as targets.
The values of the parameters that define target morphology are
computed by standard methods. For example, the area of a target is
determined simply by counting the number of locations in the
target; and the aspect ratio of
a target is determined by finding the distance (length) between the
two locations of the target that are farthest from each other,
finding the maximum width of the target in the direction
perpendicular to a line connecting those two pixels, and dividing
the length by the width. Each target is assigned to the target
class that corresponds to the values of the morphology parameters
and the spectrum type that was used to define the target.
The second preferred method of identifying and classifying targets
forms the targets by grouping together locations whose summed
intensities exceed the threshold, without regard to location
spectra. Then, within each target, each location's spectrum is
classified by spectrum type as above, and a single representative
spectrum type for the entire target is selected from among the
matching spectrum types. The simplest way to select the
representative spectrum type is by plurality: the spectrum type
that is matched to the largest number of locations within the
target is chosen as the representative spectrum type. The target
morphology type is determined as in the first method, and the
target is assigned to the target class that corresponds to the
values of the morphology parameters and the representative spectrum
type. Each target now is assigned a value of an extensive property
such as target area or total target intensity (block 54).
After all fields of view have been processed (block 56), a
descriptor vector d is formed (block 58) by summing the values of
the extensive property of the targets of each class. The vector d
has as many elements as there are target classes, and the elements
of the vector d are the sums of the extensive property values of
the targets of the corresponding target class. The last step (block
60) is to turn the descriptor vector into a concentration vector c
whose elements are the concentrations, in mass per unit area, of
the PAH species of interest. This is done using a relationship,
determined by the calibration procedure described below, between
the vectors d and c. If this relationship is determined by
multivariate analysis, then the relationship is embodied in a
matrix M such that c=dM. If this relationship is determined by
training a neural net, then d is provided to the trained neural net
as input, and c is the resulting output.
Another noteworthy difference between the present invention and the
method of particulate analysis described in co-pending U.S. patent
application Ser. No. 08/790,696 is that in the later patent
application, only fields of view in which at least one target
appears are considered. In the present invention, all fields of
view are considered, in order to obtain correct statistics
regarding the measured extensive property values.
The process of FIG. 2 is calibrated using a set of N calibration
samples, of the kind of particles that are to be analyzed. The
calibration samples may be artificial samples of known composition
or representative collections of particles, such as particles 18,
that are to be analyzed. FIG. 3 is a flow diagram of the
calibration procedure. The calibration procedure includes two loops
over the N calibration samples. In the first loop,
single-wavelength images of fields of view of the samples are
acquired. Between the two loops, the database spectrum types and
the database morphology types are determined. In the second loop,
the relationship between descriptor vectors and concentration
vectors is determined.
In the first loop (block 70), single-wavelength images of all
fields of view of each sample are acquired as described above
(blocks 40, 42, 44, 46 and 56). Images that include fluorescing
particles are saved for subsequent processing (block 72). After all
the relevant single-wavelength images of all the samples have been
collected (block 74), the spectra of locations whose summed
intensity exceeds the threshold are classified by cluster analysis
to obtain the database spectrum types (block 76). Targets are
identified as described above, the values of the morphology
parameters of each target are computed, and the database morphology
types are obtained by applying cluster analysis to the resulting
set of morphology parameter values (block 78). The database
morphology and spectrum types are used to define target classes,
and the targets in all the fields of view of al the samples are
classified according to these classes (block 80). Each target is
assigned a value of an extensive property (block 82). If the
calibration samples are artificial, then the concentrations of the
PAH species of interest are known. If the calibration samples are
representative collections, then, at the end of the first loop,
each calibration sample is analyzed by a prior art (e.g., wet
chemistry) technique to determine the concentrations therein of the
PAH species of interest (block 84).
In the second loop over samples (block 86), for each sample, a
calibration descriptor vector d.sub.n is formed (block 88) by
summing the values of the extensive property of the targets of each
class. (n.di-elect cons.[1,N] is the index of the sample.) A
calibration concentration vector c.sub.n is formed from the
concentrations of the PAH species in the sample (block 90). After
calibration descriptor vectors and calibration concentration
vectors have been determined for all N calibration samples (block
92), a collective relationship between the descriptor vectors and
the calibration vectors is determined (block 94). As noted above,
under multivariate analysis this relationship is expressed as the
matrix M that comes closest to giving c.sub.n =d.sub.n M for all N
samples. The simplest way to obtain M is by unweighted linear least
squares. Form a matrix C whose rows are the vectors c.sub.n. Form a
matrix D whose rows are the vectors d.sub.n. The desired matrix M
should come close to satisfying the equation
The unweighted linear least squares solution of equation (2) for M
is the generalized inverse solution for M. Multiplying both sides
by the transpose of D, D.sup.T, gives
The right hand side of equation (3) now is a product of M with a
square matrix D.sup.T D. Left-multiplying both sides of equation
(3) by (D.sup.T D).sup.-1 gives
Other, more sophisticated methods of approximating M within the
scope of multivariate analysis include principal component
regression and partial least squares. See, for example, H. Martens
and T. Naes, Multivariate Calibration (John Wiley & Sons,
1989).
Alternatively, a neural network is trained, using the calibration
descriptor vectors and calibration concentration vectors as a
training set. The desired relationship between descriptor vectors
and concentration vectors then is the trained neural network. See,
for example, P. Yu. V. Anastassopoulos and A. N. Venetsanopoulos,
"Pattern classification and recognition based on morphology and
neural networks", Can. J Elect. and Comp. Eng., Vol. 17 No. 2
(1992) pp. 58-59 and the references therein.
As noted above, the scope of the present invention includes
quantitation of both chemical species and biological species. The
procedure described above for analysis of PAH on aerosol particles
applies, mutatis mutandis, to analysis of airborne microorganisms.
Such analysis is important in the control of indoor air pollution
in environments, such as airports, with closed air circulation
systems.
FIG. 4A shows the experimentally determined fluorescence spectrum,
in arbitrary intensity units, of an algal species collected as
airborne particulates. FIG. 4B shows the experimentally determined
fluorescence spectrum, also in arbitrary intensity units, of
another algal species, also collected as airborne particulates. The
spectrum of FIG. 4B has two peaks, at about 520 nm and about 675
nm, corresponding to juvenile and mature members of the species.
Such spectra can be used for the classification of airborne
microorganisms in the same way that chemical fluorescence spectra
can be used to classify chemical species on aerosol particles.
While the invention has been described with respect to a limited
number of embodiments, it will be appreciated that many variations,
modifications and other applications of the invention may be
made.
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